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firecrawl-mcp-server

firecrawl_search_feedback

Provide detailed feedback on search results within 2 minutes to help improve quality and receive a credit refund. Include ratings, valuable sources, missing content, and query suggestions.

Instructions

Send structured feedback on a previous firecrawl_search result. Call this immediately after a search where you used the results so we can improve search quality and refund 1 credit (search costs 2).

Pass the searchId returned by firecrawl_search (the id field on the response) and tell us:

  • rating — overall result quality: good, partial, or bad.

  • valuableSources — which result URLs were actually useful, and a short reason why.

  • missingContentthe most important field. An ARRAY of specific pieces of content you expected to find but didn't. One entry per missing piece, each with a short topic and an optional longer description. Examples: {"topic":"enterprise pricing","description":"no pricing tier table for the Enterprise plan was returned"}, {"topic":"API rate limits"}, {"topic":"comparison vs competitors"}. Be specific — these aggregate across teams and tell us what to index next. Do not pack multiple topics into one entry.

  • querySuggestions — how the query or response shape could be improved (e.g. "would have liked official docs first", "should boost github.com").

Substantive-feedback requirement (zero-effort feedback is rejected with HTTP 400):

  • good — must include at least one valuableSources entry

  • partial — must include valuableSources or at least one missingContent entry

  • bad — must include at least one missingContent entry or querySuggestions

Time window: Feedback must be submitted within ~2 minutes of the search. Beyond that, the call returns HTTP 409 with feedbackErrorCode: "FEEDBACK_WINDOW_EXPIRED" — do not retry, just move on. Same goes for any 4xx response: do not retry-loop.

Behaviors:

  • Idempotent per searchId. Re-submitting for the same id returns alreadySubmitted: true with creditsRefunded: 0.

  • Refund only applies to billable searches; preview teams are blocked.

  • Failed searches cannot receive feedback (the search itself already returned an error you can act on).

  • Daily refund cap (per team, per UTC day, default 100 credits). Once a team's creditsRefundedToday reaches dailyRefundCap, the response returns dailyCapReached: true with creditsRefunded: 0. The feedback is still recorded for search-quality improvement — only the credit refund is gated. Stop calling this tool for the rest of the UTC day when you see dailyCapReached: true.

When to call: Right after processing a search result. If the result didn't help, send rating bad with a clear missingContent — that is just as valuable as a good rating.

Usage Example (good rating with valuable sources + missing content):

{
  "name": "firecrawl_search_feedback",
  "arguments": {
    "searchId": "0193f6c5-1234-7890-abcd-1234567890ab",
    "rating": "good",
    "valuableSources": [
      { "url": "https://docs.firecrawl.dev/features/search", "reason": "Most up-to-date description of /search." }
    ],
    "missingContent": [
      { "topic": "Pricing for the search endpoint", "description": "No pricing tier table for /search specifically." },
      { "topic": "Rate limits", "description": "Per-team RPS for /search not documented." }
    ],
    "querySuggestions": "Boost docs.firecrawl.dev for queries that mention 'firecrawl'"
  }
}

Usage Example (bad rating, what was missing):

{
  "name": "firecrawl_search_feedback",
  "arguments": {
    "searchId": "0193f6c5-1234-7890-abcd-1234567890ab",
    "rating": "bad",
    "missingContent": [
      { "topic": "Recent benchmarks", "description": "All results were >12 months old." },
      { "topic": "Comparison vs Algolia" }
    ]
  }
}

Returns: { success, feedbackId, creditsRefunded, creditsRefundedToday, dailyRefundCap, dailyCapReached?, alreadySubmitted?, warning? } JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ratingYes
searchIdYes
missingContentNoArray of specific pieces of content the agent expected to find but did not. One entry per distinct topic. Each entry has a short `topic` and optional longer `description`.
valuableSourcesNo
querySuggestionsNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant behavioral context beyond annotations: idempotent per searchId, refund only for billable searches, daily refund cap (100 credits), behavior on cap (still records feedback), time window expiry. Annotations (readOnlyHint=false, destructiveHint=false) are not contradicted; description complements them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with headings, bullet points, and examples. It is front-loaded with purpose and immediate usage. Every sentence adds value, covering all aspects without redundancy. Despite length, it remains clear and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters (2 required), no output schema, and nested objects, the description is thorough. It explains return fields (success, feedbackId, creditsRefunded, etc.) even without an output schema. It covers edge cases (time window, idempotency, cap, 4xx handling). For a non-trivial tool, this is highly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at only 20%, the description compensates fully. It explains the `rating` enum values, `valuableSources` structure, `missingContent` as 'most important field' with examples, `querySuggestions` usage. It also provides substantive-feedback requirements per rating. The detailed usage examples further clarify parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Send structured feedback on a previous `firecrawl_search` result.' It specifies the verb (send feedback), resource (search result), and includes context for quality improvement and credit refund. This distinguishes it from siblings like `firecrawl_feedback`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to call: 'Call this immediately after a search where you used the results' and provides when-not-to-call conditions: time window (~2 minutes), failed searches, daily cap reached, with proper responses (HTTP 409, etc.). It also advises not to retry on 4xx, giving clear guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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